Boosting the Sliding Frank-Wolfe solver for 3D deconvolution
Image and Video Processing
2020-09-14 v1 Machine Learning
Abstract
In the context of gridless sparse optimization, the Sliding Frank Wolfe algorithm recently introduced has shown interesting analytical and practical properties. Nevertheless, is application to large data, such as in the case of 3D deconvolution, is computationally heavy. In this paper, we investigate a strategy for leveraging this burden, in order to make this method more tractable for 3D deconvolution. We show that a boosted SFW can achieve the same results in a significantly reduced amount of time.
Keywords
Cite
@article{arxiv.2009.05473,
title = {Boosting the Sliding Frank-Wolfe solver for 3D deconvolution},
author = {Jean-Baptiste Courbot and Bruno Colicchio},
journal= {arXiv preprint arXiv:2009.05473},
year = {2020}
}
Comments
in Proceedings of iTWIST'20, Paper-ID: 08, Nantes, France, December, 2-4, 2020